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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - Analytics - **Company:** Machinify, Inc. - **Location:** Beverly Hills, CA, United States - **Experience:** Expert - **Salary:** $170,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** Microsoft Excel, Artificial Intelligence, Airflow, Data Analysis, Business Logic, Automation of Tests, BigQuery, Directed Acyclic Graph (Directed Graphs), Information Engineering, Software Design Documents, Dimensional Modeling, Apache Hive, Python (Programming Language), Operational Data Store, Power BI, Microsoft SharePoint, SQL Databases, Tableau (Software), Large Language Models, Snowflake, AI Coding Agents, Git, Drift Detection, Looker Analytics, Databricks, Web Api - **Published:** November 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-data-engineer-analytics-beverly-hills-ca--9ba473a7-9ddf-4ee6-8d7d-761fcb2844d7 ## About the Role Data Analysis, Health Plan, Artificial Intelligence (AI), Healthcare, Metrics, Warehousing, Data Analysis, Data Modeling, Power BI, Reporting Dashboards ## Description * Run discovery with business stakeholders. Find the real need behind the request and turn it into acceptance criteria you can build against. * Model noisy healthcare operational data into certified dimensional models across our Databricks bronze silver gold architecture in dbt and SQL. Our sources disagree with each other in ways that are interesting rather than trivial. Sorting that out is most of the job. * Own metric definitions. One measure, one definition, one place, documented so a non-engineer can read it. Today a single operating metric can be re-derived in a dozen models; collapsing that into a governed semantic layer is yours. * Build and own the visualization layer. Power BI semantic models, dashboards, and paginated reports, including refresh health and semantic-model currency. We want real visualization judgment: the right chart for the question, a hierarchy an executive can read in ten seconds, and a reason for every choice. * Own observability for your models. Freshness and latency detection, statistical anomaly detection, and value-drift monitoring, plus the data tests, regression guards, and CI checks that keep a defect you just fixed from coming back. * Own orchestration for what you build. dbt Cloud job design, Airflow DAGs, scheduling, dependency ordering, dependable incremental runs. * Write it down. Design documents, model documentation, a CI-enforced knowledge base, and business-readable handoff packets, so whoever inherits your work doesn't have to re-derive it. * Automate the recurring work. We codify repeating work into repeatable, testable, AI-assisted workflows instead of checklists in someone's notes. You should be comfortable directing LLMs and supervising what they produce. What You Bring * 6+ years across analytics engineering, BI engineering, data engineering, or business analysis, with production ownership of the models and metrics you built. * Expert SQL and strong dimensional modeling instincts. You spot a grain mismatch in someone else's model before you run it. * Production dbt: models, tests, macros, exposures, documentation, and CI. You have opinions about project structure and can defend them. * Hands-on with a cloud lakehouse or warehouse. Databricks and Spark SQL preferred; Snowflake, BigQuery, or Redshift transfer well. * Real visualization talent. You've owned a BI semantic layer and the reports on top of it, not just the tables underneath. Power BI and DAX strongly preferred; Looker/LookML, Tableau, MetricFlow, or Cube are credible substitutes. Bring dashboards you designed and be ready to say what you left out. * Stakeholder fluency. You can run a requirements conversation with a non-technical owner, turn a vague complaint into a testable definition, explain a data problem as business risk, and tell someone their metric is wrong without losing them. * A reconciliation reflex. When two numbers disagree you don't average them, escalate them, or trust the newer one. You find out why. * Git and pull-request discipline as normal practice rather than overhead. * Python where it's genuinely the right tool (API calls, file parsing, statistics, report generation), and the judgment to reach for SQL where it isn't. * Comfort with high autonomy in a fast, async, Slack-first environment. You decide inside your lane and inform, rather than queuing decisions for permission. * Strong writing. Much of your impact is a document, a definition, or a chart someone reads without you in the room. Bonus points for * Healthcare data: claims (837/835/UB04), DRG and coding review, payment integrity, provider contracts, or revenue cycle. * Data contracts, schema enforcement, or ODCS-style governance; quality gating in CI. * Databricks Unity Catalog, lineage, and cost/DBU accountability. * Paginated reporting, SharePoint/Excel delivery, or other "the business needs it in exactly this format" surfaces. * Working alongside AI coding agents and reviewing what they produce against a contract you wrote. Why Join Us * Own a real greenfield problem. We're building a governed semantic layer that centralizes business logic. It's a funded priority and one of the highest-impact problems on the team. * Strong engineering foundation. We already have 634 production models, 1,800+ automated tests, and strong CI guardrails. You can focus on solving new problems instead of building basic engineering discipline from scratch. * See your work reach the business. This team owns the work all the way to the reporting layer, so you'll see how your decisions affect real business users. * Work directly with stakeholders. Every project has a clear business owner and defined acceptance criteria, so you know who you're building for and what success looks like. * Use AI in the actual engineering workflow. We've built agent-driven workflows into our day-to-day development process, and our delivery velocity has grown significantly as a result. If you're interested in actually using AI to change how software gets built, you'll have plenty of opportunity here. * Make an impact in healthcare. The systems you build help a large-scale healthcare payment integrity business understand and improve its performance.